System and methods for sequential scan parameter selection

ABSTRACT

Methods and systems are provided for sequentially selecting scan parameter values for ultrasound imaging. In one example, a method includes selecting a first parameter value for the a first scan parameter based on an image quality of each ultrasound image of a first plurality of ultrasound images of an anatomical region, each ultrasound image of the first plurality of ultrasound images having a different parameter value for the first scan parameter, selecting a second parameter value for a second scan parameter based on an image quality of each ultrasound image of a second plurality of ultrasound images of the anatomical region, each ultrasound image of the second plurality of ultrasound images having a different parameter value for the second scan parameter, and applying the first parameter value for the first scan parameter and the second parameter value for the second scan parameter to one or more additional ultrasound images.

TECHNICAL FIELD

Embodiments of the subject matter disclosed herein relate to ultrasoundimaging, and more particularly, to improving image quality forultrasound imaging.

BACKGROUND

Medical ultrasound is an imaging modality that employs ultrasound wavesto probe the internal structures of a body of a patient and produce acorresponding image. For example, an ultrasound probe comprising aplurality of transducer elements emits ultrasonic pulses which reflector echo, refract, or are absorbed by structures in the body. Theultrasound probe then receives reflected echoes, which are processedinto an image. Ultrasound images of the internal structures may be savedfor later analysis by a clinician to aid in diagnosis and/or displayedon a display device in real time or near real time.

SUMMARY

In one embodiment, a method includes selecting a first parameter valuefor the a first scan parameter based on an image quality of eachultrasound image of a first plurality of ultrasound images of ananatomical region, each ultrasound image of the first plurality ofultrasound images having a different parameter value for the first scanparameter, selecting a second parameter value for a second scanparameter based on an image quality of each ultrasound image of a secondplurality of ultrasound images of the anatomical region, each ultrasoundimage of the second plurality of ultrasound images having a differentparameter value for the second scan parameter, and applying the firstparameter value for the first scan parameter and the second parametervalue for the second scan parameter to one or more additional ultrasoundimages.

The above advantages and other advantages, and features of the presentdescription will be readily apparent from the following DetailedDescription when taken alone or in connection with the accompanyingdrawings. It should be understood that the summary above is provided tointroduce in simplified form a selection of concepts that are furtherdescribed in the detailed description. It is not meant to identify keyor essential features of the claimed subject matter, the scope of whichis defined uniquely by the claims that follow the detailed description.Furthermore, the claimed subject matter is not limited toimplementations that solve any disadvantages noted above or in any partof this disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

Various aspects of this disclosure may be better understood upon readingthe following detailed description and upon reference to the drawings inwhich:

FIG. 1 shows a block diagram of an exemplary embodiment of an ultrasoundsystem;

FIG. 2 is a schematic diagram illustrating a system for generatingultrasound images at optimized parameter settings, according to anexemplary embodiment;

FIG. 3 is a schematic diagram illustrating a process for selectingultrasound scan parameters, according to an exemplary embodiment;

FIG. 4 shows example ultrasound images and associated image qualitymetrics that may be acquired according to the process of FIG. 3;

FIGS. 5A and 5B are a flow chart illustrating an example method forselecting ultrasound scan parameters during ultrasound imaging,according to an exemplary embodiment;

FIG. 6 is a flow chart illustrating an example method for selectingimage post-acquisition processing parameters during ultrasound image;

FIG. 7 is a graph showing a scan sequence for acquiring a singleultrasound image, according to an exemplary embodiment; and

FIG. 8 is a graph showing a scan sequence for acquiring multipleultrasound images, according to an exemplary embodiment.

DETAILED DESCRIPTION

Medical ultrasound imaging typically includes the placement of anultrasound probe including one or more transducer elements onto animaging subject, such as a patient, at the location of a targetanatomical feature (e.g., abdomen, chest, etc.). Images are acquired bythe ultrasound probe and are displayed on a display device in real timeor near real time (e.g., the images are displayed once the images aregenerated and without intentional delay). The operator of the ultrasoundprobe may view the images and adjust various acquisition parametersand/or the position of the ultrasound probe in order to obtainhigh-quality images of the target anatomical feature (e.g., the heart,the liver, the kidney, or another anatomical feature). The acquisitionparameters that may be adjusted include transmit frequency, transmitdepth, gain, beam steering angle, beamforming strategy, and/or otherparameters. Varying the acquisition parameters to acquire an optimalimage (e.g., of desired quality) can be very challenging and is based onuser experience. Image quality variations with acquisition parameters isnot a well-studied problem. Thus, the adjustment of the acquisitionparameters by the operator in order to acquire an optimal image is oftensubjective. For example, the operator may adjust various acquisitionparameters until an image is acquired that looks optimal to theoperator, and the process of adjusting the acquisition parameters maynot be defined or repeated from exam to exam. Further, variouspost-acquisition image parameters that may affect image quality are alsoadjustable by the operator, such as bandwidth and center frequency ofthe filtering of the received ultrasound data. This subjectivity andlack of a defined process may lead to irreproducible results and, inmany ultrasound exams, images that are as high quality as possible maynot be acquired.

Thus, according to embodiments disclosed herein, the problem of imageacquisition parameter optimization and/or image post-acquisitionprocessing optimization is addressed via a feedback system based on anautomated image quality measurement algorithm that is configured toautomatically identify the acquisition parameters that will generate thebest possible image for the anatomy being imaged. The automated imagequality measurement algorithm may include an artificialintelligence-assisted feedback system to optimize the acquisitionparameters in a sequential fashion, with one parameter after the otheradjusted to arrive at an optimal acquisition parameter setting based onautomatically identified image quality metrics. For example, the optimaltransmit depth may be selected from images acquired at several depthacquisitions based on which image has the highest, depth-specific imagequality. This is followed by optimizing for frequency from imagesacquired at various frequency settings at the selected optimal depthbased on which image as the highest, frequency-specific image quality.In doing so, the optimal acquisition parameters (e.g., depth andfrequency) for a given scan plane/anatomical feature may be identifiedin a reproducible manner, which may increase consistency of imagequality across different ultrasound exams Additionally, in someexamples, different parameter values for one or more post-acquisitionprocessing parameters may be applied to an image to generate, for eachpost-acquisition processing parameter, a set of replicate images thateach have a different parameter value for that post-acquisitionprocessing parameter. The image quality may be determined for eachreplicate image, and the replicate image having the highest imagequality may be selected. The parameter value for that post-acquisitionprocessing parameter may be set as the parameter value from the selectedreplicate image and applied to subsequent images. The selection of theoptimal acquisition and/or post-acquisition parameters may simplify theoperator's workflow, which may reduce exam time and may facilitatehigher quality exams, even for more novice operators.

An example ultrasound system including an ultrasound probe, a displaydevice, and an imaging processing system are shown in FIG. 1. Via theultrasound probe, ultrasound images may be acquired and displayed on thedisplay device. As described above, the images may be acquired usingvarious acquisition scan parameters, such as frequency and depth, whichhave parameter values that may be selected to increase image quality ofthe acquired images. To select the scan parameter values, a plurality ofimages acquired at different scan parameter values may be analyzed todetermine which scan parameter values result in images having a highestimage quality. An image processing system, as shown in FIG. 2, includesone or more image quality models, such as a depth model and one or morefrequency models, which may be deployed according to the sequentialprocess shown in FIG. 3 to determine the image quality of each of theplurality of images, as shown in FIG. 4, and select scan parametervalues that will result in relatively high image quality. A method forsequentially selecting scan parameter values during ultrasound imagingis shown in FIGS. 5A and 5B. After the target scan parameter values havebeen selected, different post-acquisition processing parameter valuesmay be applied to an image to create a set of adjusted images, and theimage quality models may be deployed to determine which image from theset of adjusted images has the highest image quality and thus whichpost-acquisition processing parameter value(s) should be applied to thatand/or subsequent images, as shown by the method of FIG. 6. The imagesdisclosed herein may be acquired according to a scan sequence as shownin FIG. 7 and/or according to a scan sequence as shown in FIG. 8.

Referring to FIG. 1, a schematic diagram of an ultrasound imaging system100 in accordance with an embodiment of the disclosure is shown. Theultrasound imaging system 100 includes a transmit beamformer 101 and atransmitter 102 that drives elements (e.g., transducer elements) 104within a transducer array, herein referred to as probe 106, to emitpulsed ultrasonic signals (referred to herein as transmit pulses) into abody (not shown). According to an embodiment, the probe 106 may be aone-dimensional transducer array probe. However, in some embodiments,the probe 106 may be a two-dimensional matrix transducer array probe. Asexplained further below, the transducer elements 104 may be comprised ofa piezoelectric material. When a voltage is applied to a piezoelectriccrystal, the crystal physically expands and contracts, emitting anultrasonic spherical wave. In this way, transducer elements 104 mayconvert electronic transmit signals into acoustic transmit beams.

After the elements 104 of the probe 106 emit pulsed ultrasonic signalsinto a body (of a patient), the pulsed ultrasonic signals areback-scattered from structures within an interior of the body, likeblood cells or muscular tissue, to produce echoes that return to theelements 104. The echoes are converted into electrical signals, orultrasound data, by the elements 104 and the electrical signals arereceived by a receiver 108. The electrical signals representing thereceived echoes are passed through a receive beamformer 110 that outputsultrasound data. Additionally, transducer element 104 may produce one ormore ultrasonic pulses to form one or more transmit beams in accordancewith the received echoes.

According to some embodiments, the probe 106 may contain electroniccircuitry to do all or part of the transmit beamforming and/or thereceive beamforming. For example, all or part of the transmit beamformer101, the transmitter 102, the receiver 108, and the receive beamformer110 may be situated within the probe 106. The terms “scan” or “scanning”may also be used in this disclosure to refer to acquiring data throughthe process of transmitting and receiving ultrasonic signals. The term“data” may be used in this disclosure to refer to either one or moredatasets acquired with an ultrasound imaging system. In one embodiment,data acquired via ultrasound system 100 may be used to train a machinelearning model. A user interface 115 may be used to control operation ofthe ultrasound imaging system 100, including to control the input ofpatient data (e.g., patient medical history), to change a scanning ordisplay parameter, to initiate a probe repolarization sequence, and thelike. The user interface 115 may include one or more of the following: arotary element, a mouse, a keyboard, a trackball, hard keys linked tospecific actions, soft keys that may be configured to control differentfunctions, and a graphical user interface displayed on a display device118.

The ultrasound imaging system 100 also includes a processor 116 tocontrol the transmit beamformer 101, the transmitter 102, the receiver108, and the receive beamformer 110. The processer 116 is in electroniccommunication (e.g., communicatively connected) with the probe 106. Forpurposes of this disclosure, the term “electronic communication” may bedefined to include both wired and wireless communications. The processor116 may control the probe 106 to acquire data according to instructionsstored on a memory of the processor, and/or memory 120. The processor116 controls which of the elements 104 are active and the shape of abeam emitted from the probe 106. The processor 116 is also in electroniccommunication with the display device 118, and the processor 116 mayprocess the data (e.g., ultrasound data) into images for display on thedisplay device 118. The processor 116 may include a central processor(CPU), according to an embodiment. According to other embodiments, theprocessor 116 may include other electronic components capable ofcarrying out processing functions, such as a digital signal processor, afield-programmable gate array (FPGA), or a graphic board. According toother embodiments, the processor 116 may include multiple electroniccomponents capable of carrying out processing functions. For example,the processor 116 may include two or more electronic components selectedfrom a list of electronic components including: a central processor, adigital signal processor, a field-programmable gate array, and a graphicboard. According to another embodiment, the processor 116 may alsoinclude a complex demodulator (not shown) that demodulates the RF dataand generates raw data. In another embodiment, the demodulation can becarried out earlier in the processing chain. The processor 116 isadapted to perform one or more processing operations according to aplurality of selectable ultrasound modalities on the data. In oneexample, the data may be processed in real-time during a scanningsession as the echo signals are received by receiver 108 and transmittedto processor 116. For the purposes of this disclosure, the term“real-time” is defined to include a procedure that is performed withoutany intentional delay. For example, an embodiment may acquire images ata real-time rate of 7-20 frames/sec. The ultrasound imaging system 100may acquire 2D data of one or more planes at a significantly fasterrate. However, it should be understood that the real-time frame-rate maybe dependent on the length of time that it takes to acquire each frameof data for display. Accordingly, when acquiring a relatively largeamount of data, the real-time frame-rate may be slower. Thus, someembodiments may have real-time frame-rates that are considerably fasterthan 20 frames/sec while other embodiments may have real-timeframe-rates slower than 7 frames/sec. The data may be stored temporarilyin a buffer (not shown) during a scanning session and processed in lessthan real-time in a live or off-line operation. Some embodiments of theinvention may include multiple processors (not shown) to handle theprocessing tasks that are handled by processor 116 according to theexemplary embodiment described hereinabove. For example, a firstprocessor may be utilized to demodulate and decimate the RF signal whilea second processor may be used to further process the data, for exampleby augmenting the data as described further herein, prior to displayingan image. It should be appreciated that other embodiments may use adifferent arrangement of processors.

The ultrasound imaging system 100 may continuously acquire data at aframe-rate of, for example, 10 Hz to 30 Hz (e.g., 10 to 30 frames persecond). Images generated from the data may be refreshed at a similarframe-rate on display device 118. Other embodiments may acquire anddisplay data at different rates. For example, some embodiments mayacquire data at a frame-rate of less than 10 Hz or greater than 30 Hzdepending on the size of the frame and the intended application. Amemory 120 is included for storing processed frames of acquired data. Inan exemplary embodiment, the memory 120 is of sufficient capacity tostore at least several seconds' worth of frames of ultrasound data. Theframes of data are stored in a manner to facilitate retrieval thereofaccording to its order or time of acquisition. The memory 120 maycomprise any known data storage medium.

In various embodiments of the present invention, data may be processedin different mode-related modules by the processor 116 (e.g., B-mode,Color Doppler, M-mode, Color M-mode, spectral Doppler, Elastography,TVI, strain, strain rate, and the like) to form 2D or 3D data. Forexample, one or more modules may generate B-mode, color Doppler, M-mode,color M-mode, spectral Doppler, Elastography, TVI, strain, strain rate,and combinations thereof, and the like. As one example, the one or moremodules may process color Doppler data, which may include traditionalcolor flow Doppler, power Doppler, HD flow, and the like. The imagelines and/or frames are stored in memory and may include timinginformation indicating a time at which the image lines and/or frameswere stored in memory. The modules may include, for example, a scanconversion module to perform scan conversion operations to convert theacquired images from beam space coordinates to display spacecoordinates. A video processor module may be provided that reads theacquired images from a memory and displays an image in real time while aprocedure (e.g., ultrasound imaging) is being performed on a patient.The video processor module may include a separate image memory, and theultrasound images may be written to the image memory in order to be readand displayed by display device 118.

In various embodiments of the present disclosure, one or more componentsof ultrasound imaging system 100 may be included in a portable, handheldultrasound imaging device. For example, display device 118 and userinterface 115 may be integrated into an exterior surface of the handheldultrasound imaging device, which may further contain processor 116 andmemory 120. Probe 106 may comprise a handheld probe in electroniccommunication with the handheld ultrasound imaging device to collect rawultrasound data. Transmit beamformer 101, transmitter 102, receiver 108,and receive beamformer 110 may be included in the same or differentportions of the ultrasound imaging system 100. For example, transmitbeamformer 101, transmitter 102, receiver 108, and receive beamformer110 may be included in the handheld ultrasound imaging device, theprobe, and combinations thereof.

After performing a two-dimensional ultrasound scan, a block of datacomprising scan lines and their samples is generated. After back-endfilters are applied, a process known as scan conversion is performed totransform the two-dimensional data block into a displayable bitmap imagewith additional scan information such as depths, angles of each scanline, and so on. During scan conversion, an interpolation technique isapplied to fill missing holes (i.e., pixels) in the resulting image.These missing pixels occur because each element of the two-dimensionalblock should typically cover many pixels in the resulting image. Forexample, in current ultrasound imaging systems, a bicubic interpolationis applied which leverages neighboring elements of the two-dimensionalblock. As a result, if the two-dimensional block is relatively small incomparison to the size of the bitmap image, the scan-converted imagewill include areas of poor or low resolution, especially for areas ofgreater depth.

Ultrasound images acquired by ultrasound imaging system 100 may befurther processed. In some embodiments, ultrasound images produced byultrasound imaging system 100 may be transmitted to an image processingsystem, where in some embodiments, the ultrasound images may be analyzedby one or more machine learning models trained using ultrasound imagesand corresponding ground truth output in order to assign scanparameter-specific image quality metrics to the ultrasound images. Asused herein, ground truth output refers to an expected or “correct”output based on a given input into a machine learning model. Forexample, if a machine learning model is being trained to classify imagesof cats, the ground truth output for the model, when fed an image of acat, is the label “cat”. As explained in more detail below, if a machinelearning model is being trained to classify ultrasound images on thebasis of an image quality factor associated with depth (e.g., visibilityof certain anatomical features), the ground truth output for the modelmay be a label indicating a level of the image quality factor, e.g.,. ona scale of 1-5 with 1 being a lowest image quality level (e.g.,reflecting insufficient or inadequate depth, the least optimal depth)and 5 being a highest image quality level (e.g., reflecting sufficientdepth, the most optimal depth). Similarly, if a machine learning modelis being trained to classify ultrasound images on the basis of an imagequality factor associated with frequency (e.g., speckling), the groundtruth output for the model may be a label indicating a level of theimage quality factor, e.g,. on a scale of 1-5 with 1 being a lowestimage quality level (e.g., reflecting high/not smooth speckling, theleast optimal frequency) and 5 being a highest image quality level(e.g., reflecting low/smooth speckling, the most optimal frequency).

Although described herein as separate systems, it will be appreciatedthat in some embodiments, ultrasound imaging system 100 includes animage processing system. In other embodiments, ultrasound imaging system100 and the image processing system may comprise separate devices. Insome embodiments, images produced by ultrasound imaging system 100 maybe used as a training data set for training one or more machine learningmodels, wherein the machine learning models may be used to perform oneor more steps of ultrasound image processing, as described below.

Referring to FIG. 2, image processing system 202 is shown, in accordancewith an exemplary embodiment. In some embodiments, image processingsystem 202 is incorporated into the ultrasound imaging system 100. Forexample, the image processing system 202 may be provided in theultrasound imaging system 100 as the processor 116 and memory 120. Insome embodiments, at least a portion of image processing 202 is disposedat a device (e.g., edge device, server, etc.) communicably coupled tothe ultrasound imaging system via wired and/or wireless connections. Insome embodiments, at least a portion of image processing system 202 isdisposed at a separate device (e.g., a workstation) which can receiveimages/maps from the ultrasound imaging system or from a storage devicewhich stores the images/data generated by the ultrasound imaging system.Image processing system 202 may be operably/communicatively coupled to auser input device 232 and a display device 234. The user input device232 may comprise the user interface 115 of the ultrasound imaging system100, while the display device 234 may comprise the display device 118 ofthe ultrasound imaging system 100, at least in some examples.

Image processing system 202 includes a processor 204 configured toexecute machine readable instructions stored in non-transitory memory206. Processor 204 may be single core or multi-core, and the programsexecuted thereon may be configured for parallel or distributedprocessing. In some embodiments, the processor 204 may optionallyinclude individual components that are distributed throughout two ormore devices, which may be remotely located and/or configured forcoordinated processing. In some embodiments, one or more aspects of theprocessor 204 may be virtualized and executed by remotely-accessiblenetworked computing devices configured in a cloud computingconfiguration.

Non-transitory memory 206 may store image quality models 208, trainingmodule 210, and ultrasound image data 212. Image quality models 208 mayinclude one or more machine learning models, such as deep learningnetworks, comprising a plurality of weights and biases, activationfunctions, loss functions, gradient descent algorithms, and instructionsfor implementing the one or more deep neural networks to process inputultrasound images. For example, image quality models 208 may storeinstructions for implementing a depth model 209 and/or one or morefrequency models 211. The depth model 209 and one or more frequencymodels 211 may each include one or more neural networks. Image qualitymodels 208 may include trained and/or untrained neural networks and mayfurther include training routines, or parameters (e.g., weights andbiases), associated with one or more neural network models storedtherein.

Depth model 209 may be a neural network (e.g., a convolutional neuralnetwork) trained to identify far-field structures in the ultrasoundimages and determine if far-field structures (e.g., structuresbeyond/below the focal point of the ultrasound beam with respect to thetransducers of the ultrasound probe) are at an expected depth. Depthmodel 209 may be trained to identify the far-field structures in a scanplane/view specific manner For example, a depth model may be trained toidentify far-field structures in a four-chamber view of a heart but notin a parasternal long axis (PLAX) view of the heart. Thus, in someexamples, depth model 209 may actually comprise a plurality of depthmodels, each specific to a different scan plane or anatomical view.Depth model 209 may be trained to output a first image quality metricthat reflects a quality of an input ultrasound image as a function oftransmit acquisition depth. For example, the far-field structuresidentified by the depth model may change in appearance/visibility asdepth is changed, and the first image quality metric output by the depthmodel may reflect the appearance/visibility of these structures as anindicator of whether the depth used to acquire the ultrasound image isan optimal depth.

The one or more frequency models 211 may include one or more neuralnetworks or other machine learning models trained to output a respectivesecond image quality metric that represents an image quality factor thatchanges as a function of transmit frequency. The one or more frequencymodels 211 may include a first frequency model that assesses specklesize (referred to as a speckle model), a second frequency model thatassess key landmarks (referred to as a landmark detection model), and athird frequency model that assess global image quality relative to apopulation-wide library of ultrasound images (referred to as a globalimage quality model). The speckle model may be trained to output aspeckle image quality metric that reflects a level of smoothness ofspeckling in the input ultrasound image. As speckling smoothnessincreases as frequency increases, the speckle image quality metric mayincrease as frequency increases. The landmark detection model may betrained to output a landmark image quality metric that reflects theappearance/visibility of certain anatomical features (landmarks) in theinput ultrasound image. For example, as transmit frequency increases,certain anatomical features, such as the mitral valves, may start todecrease in image quality/appearance. Thus, the landmark detection modelmay identify the key landmarks in the input ultrasound image and outputthe landmark image quality metric based on the image quality/visibilityof the identified key landmarks. Because the key landmarks change as thescan plane/anatomical view change, the landmark detection model mayinclude a plurality of different landmark detection models, eachspecific to a different scan plane or anatomical view.

The global image quality model may be trained to assess the overallimage quality of an input ultrasound image relative to a population-widelibrary of ultrasound images. For example, the global image qualitymodel may be trained with a plurality of ultrasound images of aplurality of different patients, with each training ultrasound imageannotated or labeled by an expert (e.g., cardiologist or otherclinician) with an overall image quality score (e.g., on a scale of 1-5with 1 being a lowest image quality and 5 being a highest imagequality). The global image quality model, after training/validation, maythen generate an output of a global image quality metric that reflectsthe overall image quality of an input ultrasound image relative to thetraining ultrasound images. By including an image quality metric thatreflects image quality relative to a wider population, patient-specificimage quality issues may be accounted for.

Non-transitory memory 206 may further include training module 210, whichcomprises instructions for training one or more of the machine learningmodels stored in image quality models 208. In some embodiments, thetraining module 210 is not disposed at the image processing system 202.The image quality models 208 thus includes trained and validatednetwork(s).

Non-transitory memory 206 may further store ultrasound image data 212,such as ultrasound images captured by the ultrasound imaging system 100of FIG. 1. The ultrasound image data 212 may comprise ultrasound imagedata as acquired by the ultrasound imaging system 100, for example. Theultrasound images of the ultrasound image data 212 may compriseultrasound images that have been acquired by the ultrasound imagingsystem 100 at different parameter values for different scan parameters,such as different frequencies and/or different depths. Further,ultrasound image data 212 may store ultrasound images, ground truthoutput, iterations of machine learning model output, and other types ofultrasound image data that may be used to train the image quality models208, when training module 210 is stored in non-transitory memory 206. Insome embodiments, ultrasound image data 212 may store ultrasound imagesand ground truth output in an ordered format, such that each ultrasoundimage is associated with one or more corresponding ground truth outputs.However, in examples where training module 210 is not disposed at theimage processing system 202, the images/ground truth output usable fortraining the image quality models 210 may be stored elsewhere.

In some embodiments, the non-transitory memory 206 may includecomponents disposed at two or more devices, which may be remotelylocated and/or configured for coordinated processing. In someembodiments, one or more aspects of the non-transitory memory 206 mayinclude remotely-accessible networked storage devices configured in acloud computing configuration.

User input device 232 may comprise one or more of a touchscreen, akeyboard, a mouse, a trackpad, a motion sensing camera, or other deviceconfigured to enable a user to interact with and manipulate data withinimage processing system 202. In one example, user input device 232 mayenable a user to make a selection of an ultrasound image to use intraining a machine learning model, to indicate or label a position of aninterventional device in the ultrasound image data 212, or for furtherprocessing using a trained machine learning model.

Display device 234 may include one or more display devices utilizingvirtually any type of technology. In some embodiments, display device234 may comprise a computer monitor, and may display ultrasound images.Display device 234 may be combined with processor 204, non-transitorymemory 206, and/or user input device 232 in a shared enclosure, or maybe peripheral display devices and may comprise a monitor, touchscreen,projector, or other display device known in the art, which may enable auser to view ultrasound images produced by an ultrasound imaging system,and/or interact with various data stored in non-transitory memory 206.

It should be understood that image processing system 202 shown in FIG. 2is for illustration, not for limitation. Another appropriate imageprocessing system may include more, fewer, or different components.

Turning to FIG. 3, it shows a process 300 for sequentially selectingscan parameter values for two scan parameters, herein depth andfrequency. The scan parameter values may be selected according to whichscan parameter values result in a highest image quality, where the imagequality is determined by machine learning models. Process 300 may becarried out with the components of FIGS. 1-2, e.g., images may beacquired via ultrasound probe 106 and the parameter value selection maybe carried out by image processing system 202. As shown, imageacquisition may commence at 304 once the operator of the ultrasoundsystem positions the ultrasound probe to image a target scan plane(shown at 302). The image acquisition may include acquiring a firstplurality of images, with each image of the first plurality of imagesacquired at a single frequency (e.g., 1.4 Mz) and a different depthvalue (e.g., a first image at 30 cm, a second image at 17 cm, and athird image at 10 cm). Each image of the first plurality of images isentered into a depth model 306, which may be a non-limiting example ofdepth model 209 of FIG. 2. The depth model 306 may be a machine learningmodel, such as a neural network, trained to determine a first imagequality score that reflects a quality of an input image from theperspective of whether one or more key anatomical features in the targetscan plane are sufficiently visible/of high enough quality. The one ormore key anatomical features may be features that change invisibility/quality as depth changes, and thus may be used as a markerfor which depth value may result in a high quality image. The image fromthe first plurality of images having the highest first image qualityscore may be selected, and the depth at which that image was acquiredmay be chosen as a selected depth value to be used for subsequent imageacquisition. If there are two or more images having the same, highestfirst image quality score, the image acquired at the largest depth maybe selected.

Once a selected depth value is identified, the transmit depth of theultrasound probe may be adjusted to the selected (e.g., optimal) depthvalue at 308, and a second plurality of images is acquired. Each imageof the second plurality of images is acquired at the selected depthvalue, and a at different frequency value (e.g., a first image may beacquired at 1.4 MHz, a second image may be acquired at 1.7 MHz, a thirdimage may be acquired at 2 MHz, and a fourth image may be acquired at2.3 MHz). Each image of the second plurality of images is entered intoone or more models to determine a second image quality score for eachimage of the second plurality of images. The one or more models mayinclude a global image quality model 310, a landmark detection model312, and a speckle model 314 (which may be non-limiting examples of theglobal image quality model, landmark detection model, and speckle modeldescribed above with respect to FIG. 2). Each of the global imagequality model 310, landmark detection model 312, and speckle model 314may be a machine learning model, such as a neural network. The globalimage quality model 310 may be trained to assign a score (e.g., on ascale of 1-5) to each image based on the image quality of that image ina population-wide manner The landmark detection model 312 and specklemodel 314 may each output scores that reflect how much a given patient'simages change in quality as a function of ultrasound transmit frequency,as speckles on ultrasound images may smooth/change in size as frequencyincreases and certain key anatomical landmarks may become more or lessvisible as frequency changes. The scores output from each model may becombined to generate a cumulative, second image quality score as shownat 316. The image from the second plurality of images having the highestsecond image quality score may be selected at 318, and the frequency atwhich that image was acquired may be chosen as a selected frequencyvalue for subsequent image acquisition. If there are two or more imageshaving the same, highest second image quality score, the image acquiredat the highest frequency may be selected.

Any additional images of that target scan plane or view desired by theoperator and/or dictated by a scanning protocol may be acquired at theselected depth value and the selected (e.g., optimal) frequency value,as shown at 320.

The sequential process shown in FIG. 3 may result in a total of m+nimages being acquired in order to select the depth value and frequencyvalue for subsequent acquisition. The m number of images may be based onhow many depth values are available/selected to be optimized. Asdescribed above, there may be three possible depth values, but othernumbers of depth values may be possible, such as two or four or more.Likewise, the n number of images may be based on how many frequencyvalues are available/selected to be optimized. As described above, theremay be four possible frequency values, but other numbers of frequencyvalues may be possible, such as three, five, etc. Further, while FIG. 3shows a process for selecting values of two scan parameters, more scanparameters may be selected/optimized, such as gain, beam steering,beamforming strategy, filtering, etc. In such examples, the number ofacquired images may be m+n+1, where the 1 number of images is based onthe values of gain (or other parameter) available to be optimized.During the depth and frequency optimization, gain may be held constantand then once depth and frequency are optimized, the 1 number of imagesmay be acquired with the optimal depth and optimal frequency and thedifferent gain values. Further still, while FIG. 3 shows depth beingoptimized before frequency, in some examples, frequency may be optimizedbefore depth is optimized.

The sequential process described above may result in fewer images beingacquired than a joint process where m x n images are acquired and thusan image is acquired for each different possible combination of scanvalues, which may make the sequential process more practical and easierto implement than a joint process. Further, each parameter that isoptimized according to the sequential process may be optimized withimages acquired over a single cardiac cycle, which may result in imageshaving fewer motion related artifacts and/or result in images that aremore comparable to each other, which may make the parameter selectionmore robust. Further, after a first parameter is optimized (e.g.,depth), the image with the highest image quality (e.g., from which thedepth value is optimized) may be presented to the operator of theultrasound system and/or the optimized depth value may be presented tothe operator before the frequency is optimized, which may allow theoperator to confirm the selection of the optimal depth value or select adifferent optimal depth value, which may reduce selection errors. As anexample scenario, valve-like structures can be enhanced on acquisitionswith high frequency by increasing the gain beyond usual/preset ranges.The enhanced valves might look similar to the valves in acquisitionswith lower frequency and normal gain values. Such confounding effectsdue to interplay between different parameters makes it difficult to pickone optimal setting in the joint process.

FIG. 4 shows a plurality of ultrasound images 400 that may be acquiredand analyzed during the process illustrated in FIG. 3. The plurality ofultrasound images 400 includes a first plurality of images 410. Eachimage of the first plurality of images 410 is acquired at a differentdepth value and at the same frequency. For example, a first image 402 isacquired at a depth of 30 cm, a second image 404 is acquired at a depthof 17 cm, and a third image 406 is acquired at a depth of 10 cm. Each ofthe first, second, and third images is acquired at a frequency of 1.4MHz. Each image in the first plurality of images 410 includes a depthscore determined based on output from the depth model, for example.

The plurality of ultrasound images 400 further includes a secondplurality of images 420. Each image of the second plurality of images420 is acquired at the same depth value and at a different frequency.For example, the second plurality of images 420 includes a first image412 acquired at a frequency of 1.4 MHz, a second image 414 acquired at afrequency of 1.7 MHz, a third image 416 acquired at a frequency of 2MHz, and a fourth image 418 acquired at a frequency of 2.3 MHz. Eachimage of the second plurality of images 420 shown in FIG. 4 includes twoquality indicators, a predicted IQ rating and a cumulative score. Thecumulative score may be determined based on output from one or more ofthe models illustrated in FIG. 3, such as the landmark detection model,the global image quality model, and the speckle size model. In someexamples, the cumulative score may further be based on the output fromthe depth model. The predicted IQ rating may be obtained from thecumulative scores using empirical thresholds determined during thetraining process. The predicted IQ rating classifies the inputultrasound image or cine loop into three levels of image quality, suchas 1 for bad/low image quality, 2 for acceptable image quality, and 3for good/high image quality, while the cumulative score is a continuousnumber which is used to select the best acquisition from a set ofacquisitions.

The image having the highest score (e.g., the depth score) from thefirst plurality of images may be selected, and the optimal depth valuemay be the depth value used to acquire the selected image. For example,as shown, second image 404 has a depth score of 5, which is higher thanthe scores of first image 402 and third image 406. Thus, the optimaldepth value may be 17 cm, as second image 404 was acquired with a depthvalue of 17 cm. When acquiring the second plurality of images 420, theultrasound probe may be controlled to a depth of 17 cm, and thus eachimage of the second plurality of images 420 is acquired at the optimaldepth of 17 cm.

Likewise, the image having the highest score (e.g., the predicted IQrating and/or the cumulative score) from the second plurality of imagesmay be selected, and the optimal frequency value may be the frequencyvalue used to acquire the selected image. For example, as shown, secondimage 414 has a predicted IQ score of 2 and a cumulative score of 4.8,which results in a higher combined score than the combined scores offirst image 412, third image 416, and fourth image 418. Thus, theoptimal frequency value may be 1.7 MHz, as second image 414 was acquiredwith a frequency value of 1.7 MHz. In some examples, only the cumulativescore may be used to select the image with the optimal frequency value.

FIGS. 5A and 5B show flow chart illustrating an example method 500 forsequential ultrasound imaging parameter selection according to anembodiment. In particular, method 500 relates to acquiring a pluralityof ultrasound images at different parameter values of different scanparameters and then processing the acquired ultrasound images withmultiple machine learning models to select scan parameter values, whichmay improve the image quality of displayed ultrasound images. Method 500is described with regard to the systems and components of FIGS. 1-2,though it should be appreciated that the method 500 may be implementedwith other systems and components without departing from the scope ofthe present disclosure. Method 500 may be carried out according toinstructions stored in non-transitory memory of a computing device, suchas image processing system 202 of FIG. 2.

At 502, ultrasound images are acquired and displayed on a displaydevice. For example, the ultrasound images may be acquired with theultrasound probe 106 of FIG. 1 and displayed to an operator via displaydevice 118. The images may be acquired and displayed in real time ornear real time, and may be acquired with default or user-specified scanparameters (e.g., default depth, frequency, etc.). At 504, method 500determines if an indication that a target scan plane is being imaged hasbeen received. The target scan plane may be a scan plane specified by ascanning protocol or designated by an operator as being a target scanplane. For example, the ultrasound images may be acquired as part of anultrasound exam where certain anatomical features are imaged in certainviews/axes in order to diagnose a patient condition, measure aspects ofthe anatomical features, etc. For example, during a cardiac exam, one ormore target scan planes (also referred to as views) of the heart of apatient may be imaged. The target scan planes may include a four-chamberview, a two-chamber view (which may also be referred to as a short axisview), and a long axis view (which may also be referred to as a PLAXview or three-chamber view). One or more images may be acquired in eachscan plane and saved as part of the exam for later analysis by aclinician such as a cardiologist. When acquiring the images for theexam, the ultrasound operator (e.g., sonographer) may move theultrasound probe until the operator determines that the target scanplane is being imaged, and then the operator may enter an input (e.g.,via user interface 115) indicating that the target scan plane is beingimaged. In another example, the ultrasound system (e.g., via the imageprocessing system 202) may automatically determine that the target scanplane is being imaged. For example, each acquired ultrasound image (orsome frequency of the acquired ultrasound images, such as every fifthimage) may be entered into a detection model that is configured toautomatically detect the current scan plane. If the current scan planeis the target scan plane, the indication may be generated.

If an indication that the target scan plane is being imaged is notreceived, method 500 returns to 502 to continue to acquire and displayultrasound images (e.g., at the default or user-set scan parameters). Ifan indication is received that the target scan plane is being imaged,method 500 proceeds to 506 to acquire a first plurality of images(and/or cine loops), where each image (or cine loop) of the firstplurality of images is acquired at a different parameter value of afirst scan parameter. For example, the first scan parameter may bedepth, and thus each image of the first plurality of images may beacquired at a different depth value (e.g., 10 cm, 17 cm, 30 cm, etc.).The first plurality of images may include three images, or more thanthree images if more than three depth values are to be selected from.During acquisition of the first plurality of images, any other scanparameters (e.g., frequency, gain, etc.) may be held constant. Forexample, each image of the first plurality of images may be acquired atthe same value for a second parameter (e.g., at the same frequencyvalue, such as 1.4 MHz). During acquisition of the first plurality ofimages, the acquired images may be displayed on the display device atthe frame rate at which the images are acquired, at least in someexamples.

At 508, a first parameter-specific quality metric for each image (and/orcine loop) of the first plurality of images is determined. The firstparameter-specific quality metric may be a quality metric that changesas the value of the first scan parameter changes. For example, when thefirst scan parameter is depth, the first parameter-specific qualitymetric may change as depth changes. As indicated at 510, the firstparameter-specific quality metric may be determined from aparameter-specific model, such as depth model 209 of FIG. 2. Forexample, each image of the first plurality of images may be entered asan input to the depth model, and the depth model may output, for eachinput image, a respective first parameter-specific quality metric (e.g.,a first image quality metric as explained above with respect to FIGS. 2and 3). In some examples, the model may be selected based on the targetscan plane. For example, a first depth model may be selected when thetarget scan plane is a four-chamber view and a second depth model may beselected when the target scan plane is a two-chamber view.

At 512, a first image (or cine loop) of the first plurality of images isidentified, where the first image is the image of the first plurality ofimages having the highest first quality metric, and the correspondingfirst parameter value for the first image is set as the first selectedvalue for the first scan parameter. The corresponding first parametervalue may be the parameter value for the first scan parameter at whichthe first selected image was acquired. For example, the correspondingfirst parameter value may be the depth value at which the first selectedimage was acquired, and thus the selected depth value may be the depthvalue at which the first selected image was acquired.

At 514, a second plurality of images (and/or cine loops) are acquired,where each image of the second plurality of images is acquired at theselected value for the first scan parameter and at a different parametervalue for a second scan parameter. For example, the second scanparameter may be frequency, and thus each image of the second pluralityof images may be acquired at a different frequency value (e.g., 1.4 MHz,1.7 MHz, 2 MHz, etc.). The second plurality of images may include fourimages (and/or cine loops), or more or less than four images if more orless than four frequency values are to be selected from. Each image ofthe second plurality of images is acquired at the same parameter value(the selected parameter value) for the first scan parameter (e.g., atthe selected depth value determined at 512). During acquisition of thesecond plurality of images, the acquired images may be displayed on thedisplay device at the frame rate at which the images are acquired, atleast in some examples.

At 516, a second parameter-specific quality metric is determined foreach image (and/or cine loop) of the second plurality of images. Thesecond parameter- specific quality metric may be a quality metric thatchanges as the value of the second scan parameter changes. For example,when the second scan parameter is frequency, the secondparameter-specific quality metric may change as frequency changes. Asindicated at 518, the second parameter-specific quality metric may bedetermined from one or more parameter-specific models, such as the oneor more frequency models 211 of FIG. 2. For example, each image of thesecond plurality of images may be entered as an input to a specklemodel, a landmark detection model, and/or a global image quality model,and the models may output, for each input image, a respective sub-metric(e.g., a respective second image quality metric as explained above withrespect to FIGS. 2 and 3). The respective sub-metrics may be combined(e.g., added or averaged) to generate the second quality metric. In someexamples, the model(s) may be selected based on the target scan plane.For example, a first landmark detection model may be selected when thetarget scan plane is a four-chamber view and a second landmark detectionmodel may be selected when the target scan plane is a two-chamber view.

At 520 (shown in FIG. 5B), a second image (or cine loop) of the secondplurality of images is identified, where the second image is the imageof the second plurality of images having the highest second qualitymetric, and the corresponding second parameter value for the secondoptimal image is set as the selected value for the second scanparameter. The corresponding second parameter value may be the parametervalue for the second scan parameter at which the second image wasacquired. For example, the corresponding parameter value may be thefrequency value at which the second image was acquired, and thus theselected frequency value may be the frequency value at which the secondimage was acquired.

At 521, method 500 optionally includes setting target post-acquisitionprocessing parameters, which is explained in more detail below withrespect to FIG. 6. Briefly, once the acquisition scan parameter valueshave been selected as explained above, target values for one or morepost-acquisition processing parameters may be selected based on theimage quality of one or more sets of adjusted images. For example, animage may be replicated, with different post-acquisition processingparameter values applied to each replicate, to create a set of adjustedimages. The image from the adjusted set of images that has the highestimage quality may be selected, and the parameter value(s) for that imagemay be applied to subsequent images. Example post-acquisition processingparameters include filtering parameters (e.g., bandwidth, centerfrequency), image contrast, image gain, etc.

At 522, one or more ultrasound images are acquired at the selected valuefor the first scan parameter and the selected value for the second scanparameter. Thus, once the scan parameter values have been selected forthe target scan plane based on the determined image quality metrics asdescribed above, the selected scan parameter values may be set and anyadditional images acquired by the ultrasound probe may be acquired atthe set, selected scan parameter values. This may include setting thetransmit depth of the ultrasound probe to the selected depth value andsetting the transmit frequency of the ultrasound probe to the selectedfrequency. In some examples, the selected scan parameter values for thetarget scan plane may be saved in memory. Then, if the operator movesthe ultrasound probe so that the target scan plane is not imaged, butthen later moves the ultrasound probe back so that the target scan planeis imaged again, the previously determined selected scan parametervalues for that scan plane may be automatically applied. Additionally,if target post-acquisition processing parameters are set (e.g.,according to the method of FIG. 6), the one or more ultrasound imagesthat are acquired at 522 may be processed according to the targetpost-acquisition processing parameters, e.g., the received ultrasounddata may be filtered according to parameter values for the filtering(e.g., bandwidth and center frequency) that are determined according tothe method of FIG. 6.

At 524, method 500 determines if the current exam includes more targetplanes. The determination of whether the current exam includes moretarget planes may be made on the basis of user input. For example, theoperator may enter a user input indicating that a new scan plane isbeing imaged, that a new scan plane is about to be imaged, or that theexam is over. In other examples, the determination of whether the examincludes more target planes may be made automatically based on thesystem determining a different scan plane is being imaged or thatscanning has been terminated. If the exam does not include more targetscan planes, for example if the current exam is complete and imaging isterminated, method 500 proceeds to 526 to display acquired images,quality metrics, and selected parameter settings, and then method 500returns. It is to be understood that acquired images may be displayed at522 and/or other points during method 500. Further, the selectedparameter settings may be displayed at 520 to allow the operator to viewand confirm the parameter settings. The quality metrics may also bedisplayed at other points in time, such as at 520. Further, the imagesacquired at 522 may be archived when requested by the operator.

If the exam does include more target scan planes, method 500 proceeds to528 to determine if an indication that the next target plane is beingimaged has been received, similar to the determination made at 504 andexplained above. If the indication has not been received, method 500proceeds to 530 to continue to acquire images at the selected values forthe first and second scan parameters (e.g., as explained above withrespect to 522), and then method 500 returns to 528 to continue todetermine if the indication has been received. If the indication hasbeen received, method 500 proceeds to 532 and optionally restricts theparameter values for one or both of the first and second scanparameters. For example, as explained above, the first scan parametermay have three possible parameter values and the second scan parametermay have four possible scan values. However, once the selected parametervalues have been determined for a given scan plane, those selectedparameter values may be applied to the next target plane, thusrestricting the available values that may be optimized. For example, ifthe first target plane was a four-chamber view, and the next targetplane is a two-chamber view, one or both of the selected values may beused to acquire images in the two-chamber view. If one of the selectedvalues is used but not the other (e.g., the selected depth is used), theselection of the selected value for the other scan parameter (e.g.,frequency) may be re-performed for the next target plane. However, whenswitching from the four-chamber view to the PLAX view, for example, bothparameter values may be re-determined and thus 532 may not be performed.

At 534, 506-522 may be repeated for the next target plane. For example,a first plurality of images may be acquired of the next target scanplane, each at a different parameter value for the first scan parameter,a first quality metric may be determined for each image of the firstplurality of images, and a first image may be identified that has thehighest first quality metric. The selected value for the first scanparameter may be set as the parameter value of the first scan parameterat which the first image was acquired. Then, at the selected value forthe first scan parameter, a second plurality of images of the nexttarget scan plane may be acquired, each at a different parameter valuefor the second scan parameter, a second quality metric may be determinedfor each image of the second plurality of images, and a second image maybe identified that has the highest second quality metric. The selectedvalue for the second scan parameter may be set as the parameter value ofthe second scan parameter at which the second image was acquired. One ormore additional images of the next target scan plane may then beacquired with the selected values for the first and second scanparameters. This process may be repeated for all additional target scanplanes, until the exam is complete.

While method 500 was described above with regard to varying depth andfrequency sequentially to determine target depth and frequency valuesthat will result in a high quality image, other acquisition scanparameters may be varied according to the method described above withoutdeparting from the scope of this disclosure. For example, beamformingstrategy and frequency may be varied sequentially. Beamforming strategymay include the type of beamforming which is employed, e.g., thestrength/type of ACE processing. Example beamforming strategies (whichmay be considered the different “parameter values” for the beamformingstrategy) may include delay sum, coherent plane wave compounding, anddivergent beam. To select a target beamforming strategy and targetfrequency, a first set of images may be acquired, each at a differentbeamforming strategy and the same frequency (e.g., a first image at afirst beamforming strategy and a first frequency, a second image at asecond beamforming strategy and the first frequency, and so forth). Eachimage of the first set of images may be assigned a quality metric, asdescribed above. For example, each image may be input to the specklemodel, the landmark detection model, and/or the global image qualitymodel, and the models may output, for each input image, a respectivesub-metric. The respective sub-metrics may be combined (e.g., added oraveraged) to generate the quality metric for each image. The imagehaving the highest quality metric may be selected, and the beamformingstrategy used to acquire the selected image may be set as the targetbeamforming strategy for subsequent image acquisition. Then, a secondset of images may be acquired, each at the target beamforming strategyand a different frequency (e.g., a first image at the target beamformingstrategy and a first frequency, a second image at the target beamformingstrategy and a second frequency, and so forth). Each image of the secondset of images may be assigned a quality metric, as described above, andthe image from the second set of images having the highest qualitymetric may be selected. The frequency used to acquire that image mayselected as the target frequency and used with the target beamformingstrategy to acquire subsequent images. In examples where depth is not ascan parameter to be varied and selected, the depth model explainedabove may be omitted from the quality metric determination.

Turning now to FIG. 6, a method 600 for determining post-acquisitionprocessing parameters for ultrasound images is presented. Method 600 maybe carried out as part of method 500, for example once acquisitionparameter values for a target plane have been determined. In otherexamples, method 600 may be carried out independently of method 500, forexample in response to a user request to set post-acquisition processingparameter values. Method 600 may be carried out according toinstructions stored in non-transitory memory of a computing device, suchas image processing system 202 of FIG. 2.

At 602, ultrasound information for a single image is obtained. Theultrasound information may be acquired with an ultrasound probe inresponse to execution of method 600, or the ultrasound information maybe retrieved from memory. In one non-limiting example, the ultrasoundinformation may be ultrasound information sufficient to generate oneimage, and the ultrasound information may be obtained with targetacquisition scan parameters as discussed above (e.g., at a target depth,a target frequency, etc.).

At 604, different parameter values for a first post-acquisitionparameter are applied to the obtained ultrasound information to generatea first set of adjusted images. For example, the first post-acquisitionparameter may be a filtering center frequency, and the differentparameter values may be different center frequencies (e.g., 3.2 MHz, 3.4MHz, and 3.6 MHz, or different multiples of the transmission frequency,such as the transmission frequency, twice the transmission frequency,and three times the transmission frequency). In another example, thefirst post-acquisition parameter may be a filtering bandwidth and thedifferent parameter values may be different bandwidths (e.g., 1 MHz, 1.2MHz, and 1.4 MHz). Each different parameter value may be applied to theultrasound information to generate an image for each parameter value.For example, when the first post-acquisition parameter is the filteringcenter frequency, the first set of adjusted images may include a firstimage generated with a center frequency of 3.2 MHz, a second imagegenerated with a center frequency of 3.4 MHz, and a third imagegenerated with a center frequency of 3.6 MHz. The same ultrasoundinformation may be used to generate each image in the first set ofadjusted images. Any other post-acquisition parameters may be heldconstant at a default or commanded value.

At 606, a quality metric of each image in the first set of adjustedimages is determined. The quality metric of each image may be determinedby entering each image as input to one or more image quality models, asexplained above with respect to FIGS. 2, 3, 5A, and 5B. For example,each image may be entered as input to a global image quality model, alandmark detection model, and a speckle model, and each model may outputa respective quality sub-metric that may be summed or averaged to arriveat an overall image quality metric for each image.

At 608, the image of the first set of adjusted images having the highestimage quality metric is selected. If two or more images have the same,highest image quality metric, an additional metric may be used to selectfrom among the two or more images, such as the global image qualitymodel sub-metric. At 610, the first post-acquisition parameter is set tothe parameter value of the selected image. For example, if the selectedimage was generated with a filter center frequency of 3.2 MHz, thefilter center frequency may be set at 3.2 MHz.

At 612, the above process may be repeated for any additionalpost-acquisition parameters. For example, after selecting the firstpost-acquisition parameter value, the ultrasound information may againbe used to generate replicate images, with each replicate image having adifferent parameter value for a second post-acquisition parameter, suchas filter bandwidth, to form a second set of adjusted images. When thefirst post-acquisition parameter has been set, the images of the secondset of adjusted images may be generated with the set parameter value forthe first post-acquisition parameter. The image quality metric may bedetermined for each image in the second set of adjusted images, and theimage having the highest image quality metric may be selected. Theparameter value for the second post-acquisition parameter of the imagehaving the highest image quality metric may be set as the parametervalue for the second post-acquisition parameter. At 614, the setparameter value for each post-acquisition parameter is applied to anysubsequent images, e.g., of the current view plane. Method 600 thenends.

While method 600 was described above as including a sequential processfor selecting parameter values for two or more post-acquisitionparameters, a joint process may be used instead. In the joint process,one set of replicate images may be generated, where each image has adifferent combination of parameter values for the two or morepost-acquisition parameters. The image quality of each image may bedetermined as described above, and the image having the highest imagequality metric may be selected. The parameter values for the two or morepost-acquisition parameters used to generate the selected image may beselected and set as the parameter values for subsequent imageprocessing.

The image acquisition process used to acquire the ultrasound imagesdescribed herein may be carried out according to a suitable scansequence. FIG. 7 shows a graph 900 illustrating a first example scansequence that may be executed to acquire ultrasound information that maybe used to generate a single image. Graph 700 shows a sector scan,though other scan geometries are also possible. Each line in graph 700represents a transmit direction, and the transmits are firedsequentially from, for example, left to right. When a transmit parameteris varied, such as frequency, the transmits may all be fired at the sameparameter value (e.g., at the same frequency, P1) to generate a firstimage. Then, the frequency may be adjusted to a second frequency, andthe transmits may all be fired sequentially at the second frequency togenerate a second image.

The scan sequence of FIG. 7 would result in subsequent images beingobtained with different parameter values, when exploring for the targetparameter value. However, subsequent shots within an image are bepossible to be acquired with different parameters. This scan process foracquisition would include firing in the same transmit direction N timesto record data for that direction with N different parameter valuesbefore moving on to the next transmit direction. This scan process isshown in FIG. 8, which shows a graph 800 illustrating a second examplescan sequence that may be executed to acquire ultrasound informationthat may be used to generate multiple images. Graph 800 shows a sectorscan, though other scan geometries are also possible. Each solid line ingraph 800 represents a transmit direction and a first parameter value,while each dashed line represents a different parameter value (fired atthe transmit direction of the solid line to its left) and the transmitsare fired sequentially from, for example, left to right. Instead ofincluding only one transmit parameter value, graph 1000 includes fourtransmit parameter values, P1-P4. For example, the transmit parametermay be frequency and each parameter value may be a different frequency.

During image acquisition, the transmits may be fired sequentially, butfor each transmit direction, a transmit may be fired for each parametervalue before moving on to the next transmit direction. For example, fora first transmit direction, a transmit may be fired at P1, a transmitmay be fired at P2, a transmit may be fired at P3, and a transmit may befired at P4 (while the different solid/dashed lines are placed besideseach other for illustration purposes, it is to be understood that eachtransmit for P1-P4 for the first transmit direction would be fired atthe same transmit direction). The transmit direction may be updated to asecond transmit direction, and a set of transmits may be fired at thesecond transmit direction, one for each parameter value. The process mayrepeat until all transmit directions have been fired at all parametervalues. A first image may be generated from information acquired whilefiring at the first parameter value, a second image may be generatedfrom information acquired while firing at the second parameter value, athird image may be generated from information acquired while firing atthe third parameter value, and a fourth image may be generated frominformation acquired while firing at the fourth parameter value. Thisscan sequence for parameter exploration would fire several times in eachdirection before moving on to the next transmit direction. This mayresult in longer time to acquire all directions of an image, but wouldresult in very low lag between the different parameters that are to becompared for image quality.

The scan sequence shown in FIG. 8 may be executed during the imageacquisition for parameter selection as described above with respect toFIGS. 5A and 5B, at least in some examples. In other examples, the scansequence shown in FIG. 7 may be executed during the image acquisitionfor parameter selection.

Further, because motion of the imaged anatomical features may contributeto fluctuations in image quality, it may be desirable to obtain theimages described herein (e.g., used to determine the optimal scanparameters) during periods where motion is not occurring, or duringperiods where motion among the images is comparable. When imaging theheart, obtaining images with no motion or comparable motion may bechallenging, given the movement of the heart over the course of acardiac cycle. For example, for a patient having a heart rate of 60beats per minute, a cardiac cycle may last one second, which isapproximately the same amount of time used to acquire all the imagesdescribed herein. Thus, the decision of whether frequency is heldconstant for a duration while depth is varied to select the optimaldepth first, or whether depth is held constant while frequency is variedto select the optimal frequency first may depend on what anatomy isbeing imaged (e.g., whether the heart is being imaged, and if so, whichview of the heart). For example, when images of different frequency butthe same depth are compared to one another to determine which image hasthe highest image quality, a more reliable determination may be madewhen all the images being compared are acquired in the same relativephase of the cardiac cycle. Thus, at least in some examples, the timingof when the different images are acquired may be set so that imagesacquired at different frequencies are acquired in the same general phaseof the cardiac cycle or otherwise are subject to similar motion.

A technical effect of sequentially selecting scan parameter valuesincludes increased image quality and reduced operator workflow demandsAnother technical effect is more consistent image quality acrossmultiple exams.

In another representation, a system includes an ultrasound probe, amemory storing instructions, and a processor communicably coupled to thememory and when executing the instructions, configured to: processultrasound information obtained with the ultrasound probe into a firstset of replicate images, each replicate image processed according to adifferent post-acquisition processing parameter value of a plurality ofpost-acquisition processing parameter values for a firstpost-acquisition processing parameter; determine an image quality metricof each replicate image of the first set of replicate images; select thereplicate image having the highest image quality metric; and processadditionally acquired ultrasound information according to thepost-acquisition processing parameter value used to process theultrasound information into the selected replicate image. In an example,each replicate image of the first set of replicate images is processedfrom the same ultrasound information, such that the replicate images areidentical other than the different post-acquisition processing parametervalues used to create the replicate images. In an example, the processoris configured to, after selecting the replicate image having the highestimage quality metric: process the ultrasound information into a secondset of replicate images, each replicate image of the second set ofreplicate images processed according to a different post-acquisitionprocessing parameter value of a plurality of post-acquisition processingparameter values for a second post-acquisition processing parameter;determine an image quality metric of each replicate image of the secondset of replicate images; select the replicate image of the second set ofreplicate images having the highest image quality metric; and processadditionally acquired ultrasound information according to thepost-acquisition processing parameter value for the secondpost-acquisition processing parameter used to process the ultrasoundinformation into the selected replicate image. In an example, eachreplicate image of the first set of replicate images is processedaccording to a different parameter value for a second post-acquisitionprocessing parameter, and the additionally acquired ultrasoundinformation is processed according to the parameter value for the secondpost-acquisition processing parameter used to process the ultrasoundinformation into the selected replicate image. In an example, theultrasound information may be acquired with the ultrasound probe at afirst target scan parameter value and a second target scan parametervalue selected according to the sequential process described above withrespect to FIGS. 5A and 5B.

When introducing elements of various embodiments of the presentdisclosure, the articles “a,” “an,” and “the” are intended to mean thatthere are one or more of the elements. The terms “first,” “second,” andthe like, do not denote any order, quantity, or importance, but ratherare used to distinguish one element from another. The terms“comprising,” “including,” and “having” are intended to be inclusive andmean that there may be additional elements other than the listedelements. As the terms “connected to,” “coupled to,” etc. are usedherein, one object (e.g., a material, element, structure, member, etc.)can be connected to or coupled to another object regardless of whetherthe one object is directly connected or coupled to the other object orwhether there are one or more intervening objects between the one objectand the other object. In addition, it should be understood thatreferences to “one embodiment” or “an embodiment” of the presentdisclosure are not intended to be interpreted as excluding the existenceof additional embodiments that also incorporate the recited features.

In addition to any previously indicated modification, numerous othervariations and alternative arrangements may be devised by those skilledin the art without departing from the spirit and scope of thisdescription, and appended claims are intended to cover suchmodifications and arrangements. Thus, while the information has beendescribed above with particularity and detail in connection with what ispresently deemed to be the most practical and preferred aspects, it willbe apparent to those of ordinary skill in the art that numerousmodifications, including, but not limited to, form, function, manner ofoperation and use may be made without departing from the principles andconcepts set forth herein. Also, as used herein, the examples andembodiments, in all respects, are meant to be illustrative only andshould not be construed to be limiting in any manner.

1. A method, comprising: selecting a first parameter value for a firstscan parameter based on an image quality of each ultrasound image of afirst plurality of ultrasound images of an anatomical region, eachultrasound image of the first plurality of ultrasound images having adifferent parameter value for the first scan parameter; selecting asecond parameter value for a second scan parameter based on an imagequality of each ultrasound image of a second plurality of ultrasoundimages of the anatomical region, each ultrasound image of the secondplurality of ultrasound images having a different parameter value forthe second scan parameter; and applying the first parameter value forthe first scan parameter and the second parameter value for the secondscan parameter to one or more additional ultrasound images.
 2. Themethod of claim 1, wherein each ultrasound image of the first pluralityof ultrasound images has the same parameter value for the second scanparameter, and wherein each ultrasound image of the second plurality ofultrasound images has the first parameter value for the first scanparameter.
 3. The method of claim 1, wherein selecting the firstparameter value comprises determining a respective first image qualityvalue for each ultrasound image of the first plurality of ultrasoundimages using a first model, and selecting a first ultrasound image ofthe first plurality of ultrasound images that has a highest first imagequality value, the selected first ultrasound image having the firstparameter value for the first scan parameter; and wherein selecting thesecond parameter value comprises determining a respective second imagequality value for each ultrasound image of the second plurality ofultrasound images using one or more second models, and selecting asecond ultrasound image of the second plurality of ultrasound imagesthat has a highest second image quality value, the selected secondultrasound image having the second parameter value for the second scanparameter.
 4. The method of claim 1, wherein the first scan parametercomprises a first post-acquisition processing parameter and the secondscan parameter comprises a second post-acquisition processing parameter,and further comprising: acquiring ultrasound information with anultrasound probe, processing the ultrasound information to generate afirst set of replicate images, each replicate image of the first set ofreplicate images processed according to a different parameter value forthe first post-acquisition processing parameter in order to generate thefirst plurality of images; processing the ultrasound information togenerate a second set of replicate images, each replicate image of thesecond set of replicate images processed according to a differentparameter value for the second post-acquisition processing parameter inorder to generate the first plurality of images.
 5. The method of claim1, wherein the first scan parameter comprises depth and the second scanparameter comprises frequency, and further comprising acquiring eachimage of the first plurality of images at a different depth value andthe same frequency value and acquiring each image of the secondplurality of images a different frequency value and the same depthvalue.
 6. The method of claim 1, wherein the first scan parametercomprises beamforming strategy and the second scan parameter comprisesfrequency, and further comprising acquiring each image of the firstplurality of images at a different beamforming strategy and the samefrequency value and acquiring each image of the second plurality ofimages a different frequency value and the same beamforming strategy. 7.A method for an ultrasound system, comprising: responsive to adetermination that a target scan plane of an anatomical region iscurrently being imaged with the ultrasound system, sequentiallyselecting a target value for a first scan parameter and a target valuefor a second scan parameter based on respective image quality metricsfor each of a plurality of images of the anatomical region in the targetscan plane; and acquiring one or more additional images of theanatomical region at the selected target value for the first scanparameter and the selected target value for the second scan parameter.8. The method of claim 7, wherein sequentially selecting the targetvalue for the first scan parameter and the target value for the secondscan parameter comprises: acquiring a first plurality of images, eachimage of the first plurality of images acquired at a differentrespective value for the first scan parameter and at the same value forthe second scan parameter, determining a respective first image qualitymetric for each image of the first plurality of images and selecting afirst image of the first plurality of images that has the highest firstimage quality metric, and setting the target value for the first scanparameter as the value for the first scan parameter at which theselected first image was acquired.
 9. The method of claim 8, whereinsequentially selecting the target value for the first scan parameter andthe target value for the second scan parameter further comprises: aftersetting the target value for the first scan parameter, acquiring asecond plurality of images, each image of the second plurality of imagesacquired at the target value for the first scan parameter and at adifferent value for the second scan parameter, determining a respectivesecond image quality metric for each image of the second plurality ofimages and selecting a second image of the second plurality of imagesthat has the highest second image quality metric, and setting the targetvalue for the second scan parameter as the value for the second scanparameter at which the selected second image was acquired.
 10. Themethod of claim 9, wherein the first scan parameter comprises depth andwherein determining the respective first image quality metric comprisesdetermining the respective first image quality metric for each image ofthe first plurality of images via a depth model.
 11. The method of claim9, wherein the second scan parameter comprises frequency and whereindetermining a respective second image quality metric for each image ofthe second plurality of images comprises determining a respective secondimage quality metric for each image of the second plurality of imagesvia one or more frequency models.
 12. The method of claim 11, whereindetermining a respective second image quality metric for each image ofthe second plurality of images via one or more frequency modelscomprises, for each image of the second plurality of images: determininga first sub-metric via a global image quality model; determining asecond sub-metric via a landmark detection model; determining a thirdsub-metric via a speckle model; and generating the second image qualitymetric for that image by summing the first sub-metric, the secondsub-metric, and the third sub-metric.
 13. The method of claim 11,wherein the first scan parameter comprises beamforming strategy andwherein determining the respective first image quality metric comprisesdetermining the respective first image quality metric for each image ofthe first plurality of images via the one or more frequency models. 14.The method of claim 7, wherein the first scan parameter comprises depthand the second scan parameter comprises frequency, wherein the targetscan plane is a first target scan plane, the target value for the firstscan parameter is a first depth value, the target value for the secondscan parameter is a first frequency value, and further comprisingresponsive to a determination that a second target scan plane of theanatomical region is currently being imaged with the ultrasound system:selecting a second depth value and a second frequency value based onrespective image quality metrics for each of an additional plurality ofsequentially acquired images of the anatomical region in the secondtarget scan plane and/or based on the first depth value and the firstfrequency value; and acquiring one or more additional images of theanatomical region at the second depth value and the second frequencyvalue.
 15. A system, comprising: a memory storing instructions; and aprocessor communicably coupled to the memory and when executing theinstructions, configured to: select a first parameter value for a firstscan parameter based on an image quality of each ultrasound image of afirst plurality of ultrasound images of an anatomical region, eachultrasound image of the first plurality of ultrasound images having adifferent parameter value for the first scan parameter; select a secondparameter value for a second scan parameter based on an image quality ofeach ultrasound image of a second plurality of ultrasound images of theanatomical region, each ultrasound image of the second plurality ofultrasound images having a different parameter value for the second scanparameter; and apply the first parameter value for the first scanparameter and the second parameter value for the second scan parameterto one or more additional ultrasound images.
 16. The system of claim 15,wherein the memory stores one or more neural networks, and wherein whenexecuting the instructions, the processor is configured to input eachultrasound image of the first plurality of ultrasound images to one ormore of the neural networks to determine the respective image quality ofeach ultrasound image of the first plurality of ultrasound images, andinput each ultrasound image of the second plurality of ultrasound imagesto one or more of the neural networks to determine the respective imagequality of each ultrasound image of the second plurality of ultrasoundimages.
 17. The system of claim 15, wherein the memory stores a globalimage quality neural network, a landmark detection neural network, and aspeckle neural network, and wherein when executing the instructions, theprocessor is configured to: input each ultrasound image of the secondplurality of ultrasound images to the global image quality neuralnetwork to determine a first sub-metric for each image of the secondplurality of ultrasound images; input each ultrasound image of thesecond plurality of ultrasound images to the landmark detection neuralnetwork to determine a second sub-metric for each image of the secondplurality of ultrasound images; input each ultrasound image of thesecond plurality of ultrasound images to the speckle neural network todetermine a third sub-metric for each image of the second plurality ofultrasound images; and determine the respective image quality for eachimage of the second plurality of ultrasound images by summing therespective first sub-metric, the respective second sub-metric, and therespective third sub-metric.
 18. The system of claim 15, wherein thefirst scan parameter comprises an acquisition depth and the second scanparameter comprises an acquisition frequency, and wherein each image ofthe second plurality of images is acquired, via an ultrasound probe, atthe selected parameter value of the acquisition depth.
 19. The system ofclaim 15, further comprising an ultrasound probe, wherein the first scanparameter is a first post-acquisition processing parameter, the secondscan parameter is a second post-acquisition processing parameter, andwherein the processor, when executing the instructions, is furtherconfigured to: acquire ultrasound information with the ultrasound probe;process the ultrasound information into the first plurality ofultrasound images by processing the ultrasound information multipletimes, each with a different parameter value for the firstpost-acquisition processing parameter; process the ultrasoundinformation into the second plurality of ultrasound images by processingthe ultrasound information multiple times, each with a differentparameter value for the second post-acquisition processing parameter.20. The system of claim 15, wherein the plurality of second images areacquired, via an ultrasound probe, before acquisition of the pluralityof first images, and wherein each first image of the plurality of firstimages is acquired, via the ultrasound probe, at the selected frequencyvalue.